


import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


SCREAMING_SNAKE_CASE			: List[str]				   =					logging.getLogger(__name__)






@dataclass
class _lowerCamelCase:
       lowercase_					:      str       					=     field(
           metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
       lowercase_					:      Optional[str]       					=     field(
           default=_a,					metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
       lowercase_					:      Optional[str]       					=     field(
           default="""NER""",					metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}  )
       lowercase_					:      Optional[str]       					=     field(
           default=_a,					metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
       lowercase_					:      bool       					=     field(default=_a,					metadata={"""help""": """Set this flag to use fast tokenization."""}  )
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       lowercase_					:      Optional[str]       					=     field(
           default=_a,					metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},					)






@dataclass
class _lowerCamelCase:
       lowercase_					:      str       					=     field(
           metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}  )
       lowercase_					:      Optional[str]       					=     field(
           default=_a,					metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""},					)
       lowercase_					:      int       					=     field(
           default=1_28,					metadata={
               """help""": (
                   """The maximum total input sequence length after tokenization. Sequences longer """
                   """than this will be truncated, sequences shorter will be padded."""
               )
           },					)
       lowercase_					:      bool       					=     field(
           default=_a,					metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )



def 							UpperCamelCase_( )       ->		int:
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    _lowercase			:				Dict 						=						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
    if len(sys.argv					) == 2 and sys.argv[1].endswith('.json'					):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        _lowercase						,							_lowercase						,							_lowercase			:				Optional[Any] 						=						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
    else:
        _lowercase						,							_lowercase						,							_lowercase			:				Optional[int] 						=						parser.parse_args_into_dataclasses()

    if (
        os.path.exists(training_args.output_dir					)
        and os.listdir(training_args.output_dir					)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        raise ValueError(
            F'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
            ' --overwrite_output_dir to overcome.'					)

    _lowercase			:				Dict 						=						import_module('tasks'					)
    try:
        _lowercase			:				Optional[Any] 						=						getattr(lowerCamelCase_				,					model_args.task_type					)
        _lowercase			:				TokenClassificationTask 						=						token_classification_task_clazz()
    except AttributeError:
        raise ValueError(
            F'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
            F'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''					)

    # Setup logging
    logging.basicConfig(
        format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'				,					datefmt='%m/%d/%Y %H:%M:%S'				,					level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN				,					)
    logger.warning(
        'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'				,					training_args.local_rank				,					training_args.device				,					training_args.n_gpu				,					bool(training_args.local_rank != -1					)				,					training_args.fpaa				,					)
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank					):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info('Training/evaluation parameters %s'				,					lowerCamelCase_					)

    # Set seed
    set_seed(training_args.seed					)

    # Prepare CONLL-2003 task
    _lowercase			:				int 						=						token_classification_task.get_labels(data_args.labels					)
    _lowercase			:				Dict[int, str] 						=						dict(enumerate(lowerCamelCase_					)					)
    _lowercase			:				Union[str, Any] 						=						len(lowerCamelCase_					)

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    _lowercase			:				List[Any] 						=						AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path				,					num_labels=lowerCamelCase_				,					idalabel=lowerCamelCase_				,					labelaid={label: i for i, label in enumerate(lowerCamelCase_					)}				,					cache_dir=model_args.cache_dir				,					)
    _lowercase			:				List[Any] 						=						AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,					cache_dir=model_args.cache_dir				,					use_fast=model_args.use_fast				,					)
    _lowercase			:				List[Any] 						=						AutoModelForTokenClassification.from_pretrained(
        model_args.model_name_or_path				,					from_tf=bool('.ckpt' in model_args.model_name_or_path					)				,					config=lowerCamelCase_				,					cache_dir=model_args.cache_dir				,					)

    # Get datasets
    _lowercase			:				List[Any] 						=						(
        TokenClassificationDataset(
            token_classification_task=lowerCamelCase_				,					data_dir=data_args.data_dir				,					tokenizer=lowerCamelCase_				,					labels=lowerCamelCase_				,					model_type=config.model_type				,					max_seq_length=data_args.max_seq_length				,					overwrite_cache=data_args.overwrite_cache				,					mode=Split.train				,					)
        if training_args.do_train
        else None
    )
    _lowercase			:				Dict 						=						(
        TokenClassificationDataset(
            token_classification_task=lowerCamelCase_				,					data_dir=data_args.data_dir				,					tokenizer=lowerCamelCase_				,					labels=lowerCamelCase_				,					model_type=config.model_type				,					max_seq_length=data_args.max_seq_length				,					overwrite_cache=data_args.overwrite_cache				,					mode=Split.dev				,					)
        if training_args.do_eval
        else None
    )

    def align_predictions(lowerCamelCase_				,					lowerCamelCase_					) -> Tuple[List[int], List[int]]:
        _lowercase			:				List[str] 						=						np.argmax(lowerCamelCase_				,					axis=2					)

        _lowercase						,							_lowercase			:				Dict 						=						preds.shape

        _lowercase			:				int 						=						[[] for _ in range(lowerCamelCase_					)]
        _lowercase			:				Dict 						=						[[] for _ in range(lowerCamelCase_					)]

        for i in range(lowerCamelCase_					):
            for j in range(lowerCamelCase_					):
                if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                    out_label_list[i].append(label_map[label_ids[i][j]]					)
                    preds_list[i].append(label_map[preds[i][j]]					)

        return preds_list, out_label_list

    def compute_metrics(lowerCamelCase_					) -> Dict:
        _lowercase						,							_lowercase			:				Optional[int] 						=						align_predictions(p.predictions				,					p.label_ids					)
        return {
            "accuracy_score": accuracy_score(lowerCamelCase_				,					lowerCamelCase_					),
            "precision": precision_score(lowerCamelCase_				,					lowerCamelCase_					),
            "recall": recall_score(lowerCamelCase_				,					lowerCamelCase_					),
            "f1": fa_score(lowerCamelCase_				,					lowerCamelCase_					),
        }

    # Data collator
    _lowercase			:				Any 						=						DataCollatorWithPadding(lowerCamelCase_				,					pad_to_multiple_of=8					) if training_args.fpaa else None

    # Initialize our Trainer
    _lowercase			:				List[Any] 						=						Trainer(
        model=lowerCamelCase_				,					args=lowerCamelCase_				,					train_dataset=lowerCamelCase_				,					eval_dataset=lowerCamelCase_				,					compute_metrics=lowerCamelCase_				,					data_collator=lowerCamelCase_				,					)

    # Training
    if training_args.do_train:
        trainer.train(
            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
        trainer.save_model()
        # For convenience, we also re-save the tokenizer to the same directory,
        # so that you can share your model easily on huggingface.co/models =)
        if trainer.is_world_process_zero():
            tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
    _lowercase			:				Any 						=						{}
    if training_args.do_eval:
        logger.info('*** Evaluate ***'					)

        _lowercase			:				List[str] 						=						trainer.evaluate()

        _lowercase			:				Dict 						=						os.path.join(training_args.output_dir				,					'eval_results.txt'					)
        if trainer.is_world_process_zero():
            with open(lowerCamelCase_				,					'w'					) as writer:
                logger.info('***** Eval results *****'					)
                for key, value in result.items():
                    logger.info('  %s = %s'				,					lowerCamelCase_				,					lowerCamelCase_					)
                    writer.write('%s = %s\n' % (key, value)					)

            results.update(lowerCamelCase_					)

    # Predict
    if training_args.do_predict:
        _lowercase			:				List[Any] 						=						TokenClassificationDataset(
            token_classification_task=lowerCamelCase_				,					data_dir=data_args.data_dir				,					tokenizer=lowerCamelCase_				,					labels=lowerCamelCase_				,					model_type=config.model_type				,					max_seq_length=data_args.max_seq_length				,					overwrite_cache=data_args.overwrite_cache				,					mode=Split.test				,					)

        _lowercase						,							_lowercase						,							_lowercase			:				Dict 						=						trainer.predict(lowerCamelCase_					)
        _lowercase						,							_lowercase			:				str 						=						align_predictions(lowerCamelCase_				,					lowerCamelCase_					)

        _lowercase			:				str 						=						os.path.join(training_args.output_dir				,					'test_results.txt'					)
        if trainer.is_world_process_zero():
            with open(lowerCamelCase_				,					'w'					) as writer:
                for key, value in metrics.items():
                    logger.info('  %s = %s'				,					lowerCamelCase_				,					lowerCamelCase_					)
                    writer.write('%s = %s\n' % (key, value)					)

        # Save predictions
        _lowercase			:				Optional[int] 						=						os.path.join(training_args.output_dir				,					'test_predictions.txt'					)
        if trainer.is_world_process_zero():
            with open(lowerCamelCase_				,					'w'					) as writer:
                with open(os.path.join(data_args.data_dir				,					'test.txt'					)				,					'r'					) as f:
                    token_classification_task.write_predictions_to_file(lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)

    return results



def 							UpperCamelCase_( lowerCamelCase_					)       ->		Tuple:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
 main()







'''simple docstring'''



from collections import defaultdict
def        UpperCAmelCase (    A	:					int					):
							SCREAMING_SNAKE_CASE       :   List[Any]         =      1
							SCREAMING_SNAKE_CASE       :   Dict         =      True
							for v in tree[start]:
														if v not in visited:
																					ret += dfs(A					)
							if ret % 2 == 0:
														cuts.append(A					)
							return ret
def        UpperCAmelCase (    ):
							dfs(1					)


if __name__ == "__main__":
							lowerCAmelCase_      ,				lowerCAmelCase_  :							Dict             =    10, 9
							lowerCAmelCase_  :							Dict             =    defaultdict(list)
							lowerCAmelCase_  :							dict[int, bool]             =    {}
							lowerCAmelCase_  :							list[int]             =    []
							lowerCAmelCase_  :							Optional[Any]             =    0
							lowerCAmelCase_  :							Dict             =    [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)




